Robotics

Having a machine learning agent interact with its environment requires true
unsupervised learning, skill acquisition, active learning, exploration and
reinforcement, all ingredients of human learning that are still not well understood
or exploited through the supervised approaches that dominate deep learning today.

Our goal is to improve robotics via machine learning, and improve machine
learning via robotics. We foster close collaborations between machine learning
researchers and roboticists to enable learning at scale on real and simulated
robotic systems.

Datasets

Robot Arm Grasping
and Pushing This dataset contains recordings of 650k robotic grasp attempts
and 59k object pushing interactions. Each grasp attempt is annotated with the
success or failure of the grasp, and each push includes video and joint angle
sequences. Data was collected using real robots and several hundred different
objects.